Chat dialogues contain considerable useful information about a speaker's interests, preferences, and experiences.Thus, knowledge from open-domain chat dialogue can be used to personalize various systems and offer recommendations for advanced information.This study proposed a novel framework SumRec for recommending information from open-domain chat dialogue.The study also examined the framework using ChatRec, a newly constructed dataset for training and evaluation. To extract the speaker and item characteristics, the SumRec framework employs a large language model (LLM) to generate a summary of the speaker information from a dialogue and to recommend information about an item according to the type of user.The speaker and item information are then input into a score estimation model, generating a recommendation score.Experimental results show that the SumRec framework provides better recommendations than the baseline method of using dialogues and item descriptions in their original form. Our dataset and code is publicly available at https://github.com/Ryutaro-A/SumRec

翻译：聊天对话中包含大量关于说话者兴趣、偏好和经历的有用信息。因此，开放域聊天对话中的知识可用于个性化定制各类系统，并推荐高级信息。本研究提出了一种新颖的框架SumRec，用于从开放域聊天对话中推荐信息。研究还利用新构建的数据集ChatRec进行训练和评估。为提取说话者和物品特征，SumRec框架采用大语言模型（LLM）从对话中生成说话者信息摘要，并根据用户类型推荐物品信息。随后将说话者与物品信息输入评分估计模型，生成推荐分数。实验结果表明，与使用原始对话和物品描述的基线方法相比，SumRec框架提供了更优的推荐效果。我们的数据集和代码已在https://github.com/Ryutaro-A/SumRec 公开。